Drug delivery systems for oral disease applications

There are many restrictions on topical medications for the oral cavity. Various factors affect the topical application of drugs in the oral cavity, an open and complex environment. The complex physical and chemical environment of the oral cavity, such as saliva and food, will influence the effect of free drugs. Therefore, drug delivery systems have served as supporting structures or as carriers loading active ingredients, such as antimicrobial agents and growth factors (GFs), to promote antibacterial properties, tissue regeneration, and engineering for drug diffusion. These drug delivery systems are considered in the prevention and treatment of dental caries, periodontal disease, periapical disease, the delivery of anesthetic drugs, etc. These carrier materials are designed in different ways for clinical application, including nanoparticles, hydrogels, nanofibers, films, and scaffolds. This review aimed to summarize the advantages and disadvantages of different carrier materials. We discuss synthesis methods and their application scope to provide new perspectives for the development and preparation of more favorable and effective local oral drug delivery systems

Analyzing Generalization in Policy Networks: A Case Study with the Double-Integrator System

Extensive utilization of deep reinforcement learning (DRL) policy networks in diverse continuous control tasks has raised questions regarding performance degradation in expansive state spaces where the input state norm is larger than that in the training environment. This paper aims to uncover the underlying factors contributing to such performance deterioration when dealing with expanded state spaces, using a novel analysis technique known as state division. In contrast to prior approaches that employ state division merely as a post-hoc explanatory tool, our methodology delves into the intrinsic characteristics of DRL policy networks. Specifically, we demonstrate that the expansion of state space induces the activation function $\tanh$ to exhibit saturability, resulting in the transformation of the state division boundary from nonlinear to linear. Our analysis centers on the paradigm of the double-integrator system, revealing that this gradual shift towards linearity imparts a control behavior reminiscent of bang-bang control. However, the inherent linearity of the division boundary prevents the attainment of an ideal bang-bang control, thereby introducing unavoidable overshooting. Our experimental investigations, employing diverse RL algorithms, establish that this performance phenomenon stems from inherent attributes of the DRL policy network, remaining consistent across various optimization algorithms

Multi-objective optimal scheduling of charging stations based on deep reinforcement learning

With the green-oriented transition of energy, electric vehicles (EVs) are being developed rapidly to replace fuel vehicles. In the face of large-scale EV access to the grid, real-time and effective charging management has become a key problem. Considering the charging characteristics of different EVs, we propose a real-time scheduling framework for charging stations with an electric vehicle aggregator (EVA) as the decision-making body. However, with multiple optimization objectives, it is challenging to formulate a real-time strategy to ensure each participant’s interests. Moreover, the uncertainty of renewable energy generation and user demand makes it difficult to establish the optimization model. In this paper, we model charging scheduling as a Markov decision process (MDP) based on deep reinforcement learning (DRL) to avoid the afore-mentioned problems. With a continuous action space, the MDP model is solved by the twin delayed deep deterministic policy gradient algorithm (TD3). While ensuring the maximum benefit of the EVA, we also ensure minimal fluctuation in the microgrid exchange power. To verify the effectiveness of the proposed method, we set up two comparative experiments, using the disorder charging method and deep deterministic policy gradient (DDPG) method, respectively. The results show that the strategy obtained by TD3 is optimal, which can reduce power purchase cost by 10.9% and reduce power fluctuations by 69.4%

Copper-based charge transfer multiferroics with a d9d^9 configuration

Multiferroics are materials with a coexistence of magnetic and ferroelectric order allowing the manipulation of magnetism by applications of an electric field through magnetoelectric coupling effects. Here we propose an idea to design a class of multiferroics with a $d^9$ configuration using the magnetic order in copper-oxygen layers appearing in copper oxide high-temperature superconductors by inducing ferroelectricity. Copper-based charge transfer multiferroics SnCuO2 and PbCuO2 having the inversion symmetry breaking $P4mm$ polar space group are predicted to be such materials. The active inner s electrons in Sn and Pb hybridize with O $2p$ states leading the buckling in copper-oxygen layers and thus induces ferroelectricity, which is known as the lone pair mechanism. As a result of the $d^9$ configuration, SnCuO2 and PbCuO2 are charge transfer insulators with the antiferromagnetic ground state of the moment on Cu retaining some strongly correlated physical properties of parent compounds of copper oxide high-temperature superconductors. Our work reveals the possibility of designing multiferroics based on copper oxide high-temperature superconductors.Comment: 18 pages, 5 figures, 1 tabl

Elemental topological ferroelectrics and polar metals of few-layer materials

Ferroelectricity can exist in elemental phases as a result of charge transfers between atoms occupying inequivalent Wyckoff positions. We investigate the emergence of ferroelectricity in two-dimensional elemental materials with buckled honeycomb lattices. Various multi-bilayer structures hosting ferroelectricity are designed by stacking-engineering. Ferroelectric materials candidates formed by group IV and V elements are predicted theoretically. Ultrathin Bi films show layer-stacking-dependent physical properties of ferroelectricity, topology, and metallicity. The two-bilayer Bi film with a polar stacking sequence is found to be an elemental topological ferroelectric material. Three and four bilayers Bi films with polar structures are ferroelectric-like elemental polar metals with topological nontrivial edge states. For Ge and Sn, trivial elemental polar metals are predicted. Our work reveals the possibility of design two-dimensional elemental topological ferroelectrics and polar metals by stacking-engineering.Comment: 18 pages, 6 figure

Total Synthesis of the Diterpenoid (+)â Harringtonolide

Described herein is the first asymmetric total synthesis of (+)â harringtonolide, a natural diterpenoid with an unusual tropone imbedded in a cagelike framework. The key transformations include an intramolecular Dielsâ Alder reaction and a rhodiumâ complexâ catalyzed intramolecular [3+2] cycloaddition to install the tetracyclic core as well as a highly efficient tropone formation.Ever more rings: The first asymmetric total synthesis of the diterpenoid (+)â harringtonolide is described. The key features include an asymmetric transfer hydrogenation, an intramolecular Dielsâ Alder reaction, chemoselective functionalization of an olefin in the presence of an acetylenic group, a rhodiumâ catalyzed intramolecular [3+2] cycloaddition, and efficient formation of the tropone.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/137303/1/anie201605879-sup-0001-misc_information.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/137303/2/anie201605879_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/137303/3/anie201605879.pd

Learning the 3D fauna of the web

Learning 3D models of all animals in nature requires massively scaling up existing solutions. With this ultimate goal in mind, we develop 3D-Fauna, an approach that learns a pan-category deformable 3D animal model for more than 100 animal species jointly. One crucial bottleneck of modeling animals is the limited availability of training data, which we overcome by learning our model from 2D Internet images. We show that prior approaches, which are category-specific, fail to generalize to rare species with limited training images. We address this challenge by introducing the Semantic Bank of Skinned Models (SBSM), which automatically discovers a small set of base animal shapes by combining geometric inductive priors with semantic knowledge implicitly captured by an off-the-shelf self-supervised feature extractor. To train such a model, we also contribute a new large-scale dataset of diverse animal species. At inference time, given a single image of any quadruped animal, our model reconstructs an articulated 3D mesh in a feed-forward manner in seconds

Learning the 3D fauna of the web

Learning 3D models of all animals on the Earth requires massively scaling up existing solutions. With this ultimate goal in mind, we develop 3D-Fauna, an approach that learns a pan-category deformable 3D animal model for more than 100 animal species jointly. One crucial bottleneck of modeling animals is the limited availability of training data, which we overcome by simply learning from 2D Internet images. We show that prior category-specific attempts fail to generalize to rare species with limited training images. We address this challenge by introducing the Semantic Bank of Skinned Models (SBSM), which automatically discovers a small set of base animal shapes by combining geometric inductive priors with semantic knowledge implicitly captured by an off-the-shelf self-supervised feature extractor. To train such a model, we also contribute a new large-scale dataset of diverse animal species. At inference time, given a single image of any quadruped animal, our model reconstructs an articulated 3D mesh in a feed-forward fashion within seconds

On the size-dependent fatigue behaviour of laser powder bed fusion Ti-6Al-4V

A sample size effect which influences the fatigue behaviour of laser powder bed fusion Ti-6Al-4V is identified and quantified. Two cylindrical samples are considered: ∅ 1.3 mm and ∅ 2.0 mm. The larger specimen demonstrates better fatigue resistance particularly in the high-cycle regime, with the differing surface roughness contributing to this effect. It is also confirmed that processing-induced porosity can compromise the fatigue performance even when the initiation sites are surface defects. The larger contribution of porosity to the fatigue fracture process of the larger specimen results in a higher scatter in the fatigue life. Differences in microstructure do not seem to contribute strongly to the variation in fatigue properties of the two specimens, but we present some evidence that the coarser microstructure of the larger specimen promotes a stronger tolerance to defects and induces more tortuous crack paths which hinders fatigue crack growth